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Location Matters! LandPKS Can Provide Point-Scale Soil Information

The Land Potential-Knowledge System (LandPKS; landpotential.org) was created to help put valuable information about the land, including climate, soils, and vegetation, in the hands of land managers across the world.  It does this through the use of the LandPKS Mobile app, which is free to download and use for both Android and Iphone.  Importantly, LandPKS is a way to both input and access data that is point-based and georeferenced. The LandInfo module is one component of the LandPKS app and allows the user to obtain information about the soil directly beneath their feet. The LandInfo module walks a user through digging a hole and hand-texturing the soil to determine the soil texture and available water holding capacity or AWC. Future versions of LandInfo will also include infiltration rates, organic matter, soil color, and have algorithms that match the user-input data about soil texture with global soil maps to provide the user with the specific name of their soil.

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The point-based model used by LandPKS is incredibly powerful because in many parts of the world the soil varies significantly from place to place and these changes in soil types can have dramatic impacts for farmers and others aiming to utilize that land.  One excellent example is from the village of Nyamihuu, located near Iringa, Tanzania (image below).  With local farmers, the LandPKS team dug three LandInfo Plots within a short distance from each other on a slightly sloping landscape.  The differences in soil texture and AWC were quite drastic, with the Lower Field having almost double the AWC of the Upper Field.  This has serious implications for farmers because the Lower Field will be generally more productive due to the greater ability to hold water in the soil for crops to utilize.  Further, the Forest plot had by far the lowest AWC, which is important because it suggests that clearing the forest for cultivation may not be worth the effort and environmental impact.

 

The lesson here is that location matters!  Soil can vary from one farm to the next, and LandPKS can empower farmers, agricultural extension agents, and others to gain access to site-specific soil information.  Knowing your soil texture and AWC can influence what decisions are made.  First, it can help a land manager decide if they want to farm a piece of land or not.  As the example of the Forest plot above shows, some land is not suitable for agriculture, and LandPKS can help provide knowledge to show this.  Second, soil texture and AWC may influence decisions about crop selection or crop varieties.  Planting crops that are suitable for their specific soil will help farmers increase production and farm more sustainably.  For example, the farmer on the Upper Field may want to plant more drought resistant crops or practice water conservation measures in order to make up for the lower AWC of their farm.  Location matters, and LandPKS is one tool that can provide point-based, georeferenced data to those who need it to make more sustainable land management decisions.  For more information about LandPKS please visit our website at landpotential.org or e-mail us at contact@landpotential.org.

Online Training for Using the LandPKS App – Available Now!

Want to learn more about using the LandPKS app?  Interested in learning about how to access, analyze, and interpret your LandPKS results on the LandPKS Data Portal (portal.landpotential.org)? Now it is as easy as accessing our new online LandPKS training at http://learn.landpotential.org/.  You can create an account to track your progress or use the training as a guest.  If you have any trouble accessing the training, please contact us as contact@landpotential.org.

The Role of LandPKS for Wildlife Conservation

Protected areas cover nearly 13% of the earth’s surface, illustrating the critical importance of conserving biodiversity globally (Barua et al. 2013).  However, wildlife habitat is not confined to protected areas and many wildlife species live and migrate in landscapes dominated by humans (Nyamwamu et al. 2015).  Thus, not only is effective protected area management important for conservation, but also maintaining human-dominated landscapes that can still function as wildlife habitat. The East African country of Kenya is world renowned for its wildlife, with over 10% of the land in national parks and much more in community-managed wildlife conservancies (Western et al. 2015).

In Kenya, the Land Potential Knowledge System (LandPKS; landpotential.org) has been providing a valuable tool to help land managers, researchers, and rangeland managers effectively conserve and restore biodiversity. LandPKS is a free, open source, mobile app that provides users with information about the potential of their land, including identifying the soil type and monitoring vegetation growth or degradation.  Using the Land Cover module, a user can track changes in vegetation cover, including the restoration of important fodder species for both livestock and wildlife, as well as monitor the abundance of invasive, destructive plant species. The LandPKS app makes monitoring important areas for both wildlife and livestock easy and convenient, with instant results delivered directly to the user’s phone.

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Managing land more effectively for wildlife also means managing land more effectively for communities that also rely on the same land for their livelihoods. LandPKS can help identify areas that are more suitable for rangeland, agriculture, wildlife conservation, and other uses. Matching land with suitable land-uses is critical for natural resource governance because it helps promote sustainable conservation and livelihoods simultaneously. Further, matching a land use to a suitable area will increase the ability of communities, and wildlife, to adapt to changing environmental conditions, such as increased drought frequency and severity. Further, in mixed-use areas of Kenya, such as the case study below, maximizing fodder species, and reducing undesirable species has benefits for both wildlife and livestock.

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In the Laikipia and Samburu Counties in Kenya, complex historical land tenure systems have resulted in a patchwork of private ranches, community-owned conservancies, and open rangeland.  Here, rangeland degradation manifests itself through increased bare ground and replacement of perennial grasses by undesirable species, such as Acacia reficiens.  LandPKS is being used here to help assess the success of invasive species control methods.  LandInfo, was used to describe selected treatment sites, and provided data for biophysical matching with control sites.  LandCover was then used to collect data on various vegetation metrics.  Using LandPKS in Westgate and Kalama Conservancies helped to identify, match, and assess treatment and control plots for large-scale mechanical clearing of unwanted species and reseeding projects.  This type of vegetation restoration benefits livestock and wildlife, and can help conserve critical biodiversity.  LandPKS is one potential tool that can contribute to wildlife conservation, as well as effective natural resource governance and land use planning.

For more information about LandPKS visit our website at landpotential.org or visit our blog at https://landpotential.blogspot.com/.

Barua, M., Bhagwat, S. A., & Jadhav, S. (2013). The hidden dimensions of human-wildlife conflict: health impacts, opportunity and transaction costs. Biological Conservation, 157, 309-316.

Nyamwamu, R.O., Mwangi, J. G., & Ombati, J. M. (2015). Untapped potential of wildlife agricultural extension mitigation strategies in influencing the extent of human-wildlife conflict: a case of smallholder agro-pastoralist in Laikipia County, Kenya. International Journal of Agricultural Extension, 3(1), 73-81.

 

Western, D., Waithaka, J., & Kamanga, J. (2015). Finding space for wildlife beyond national parks and reducing conflict through community-based conservation: the Kenya experiences. Parks, 21(1).

Manual Testing of the LandPKS App for Quality Assurance

Huong Tran is the Quality Assurance Coordinator for LandPKS. She is based in Las Cruces, NM and has been working with the project since Jan 2016. Huong’s primary roles with the project include creating test plans and test scenarios, working with software developers to ensure quality standards, and performing manual tests for various products of LandPKS. She enjoys catching LandPKS bugs before users do and is glad that she can contribute to provide confidence towards software quality.

Scenario testing is done to make sure that the end to end functioning of the LandPKS app and all the process flows are working well. In scenario testing, the tester puts herself in the end user’s shoes and figures out the real world scenarios or use cases which can be performed on the app by the end user. Scenario testing helps testers to explore how the app will work in the hands of an end user. This is very important for catching bugs in the LandPKS app and designing the app with users’ needs in mind.

Even though the main goal of testing is to be able to detect and catch many of the bugs, the automated tools cannot test for visual considerations like gestures, image color or font size. However, the manual testing that Huong does can judge these types of app features. Manual testing can also test the User Experience and User Interface. Any bugs in data connection and/or slope measurement, which are two critical functionalities of the LandPKS app, can also be caught with manual testing

In light of the fact that access to an Internet connection in Africa is still limited, Huong has prioritized her testing efforts and focused on the reliability of the apps in an unstable data connection environment. She has tried to replicate the scenarios that a common end user might face when s/he is in the field collecting data and using the app. Her tests are done to ensure that the GPS is still working well, the app is behaving as expected, and that entered data is saved when there is no network or data connection.

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Introducing Thanh: A Longtime LandPKS Team Member

Thanh Nguyen is a PhD candidate in the Computer Science Department at New Mexico State University. Thanh has been working on the LandPKS project for several years now and has taken a critical role in the development of the LandPKS app.  During his time with LandPKS, he has worked on the following:

  1. Developed Data Analytics System and Prediction Model that applied Machine Learning Algorithms to get as much knowledge as possible from the soil profile, weather, and water data to build models for analyzing soil potential.
  2. Developed the LandPKS Database and LandPKS API System that allowed developers and users to interact with and access LandPKS data (LandInfo, LandCover, etc). LandPKS Database and API System are hosted in Google Cloud Platform and people can visit: https://api.landpotential.org
  3. Developed Web Data Portal to allow users to have the ability to access and download LandPKS data in Web Browser. In addition, the Data Portal provides tools that analyze and displays the user data.
  4. Developed LandPKS mobile application that allows users to collect and interact with LandPKS data in Android and iOS.
  5. Developed Big-Data processing module using Map-Reduce (Hadoop) to create accessible climate data and soil profiles for all locations in the world

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Thanh graduated with his Masters of Computer Science at James Cook University in Australia. His thesis, entitled Data Mining in Internet Banking, is currently being used in a number of Asian banks.  His research interests include Data Mining and Knowledge Discovery (classification, clustering, association rules and prediction models), Artificial Intelligence (knowledge representation and reasoning, planning, logic programming, answer set programming and Web semantics – Services Composition), Machine Learning and Collective Intelligence (recommendation system, discovering groups, searching and ranking, collaborative filtering, document filtering, generative modelling, advanced classification, etc.) and Big-Data processing.  His primary research now focuses on Automation Web Services Composition in Web Semantics. He is developing a completed end-to-end AI system to collect requirements from users in Natural Language and explore workflows that can satisfy the users requirements automatically. After the workflow is achieved, our system is able to execute each Web Service component in workflow sequence in order to achieve the goal.

 

Introducing Brent and the LandPKS Application Programming Interface (API) System

Brent, an undergraduate student at New Mexico State University, has been working with LandPKS on the API (Application Programming Interface) and an automated build and testing suite. Whenever a change is made to any LandPKS app, the automation suite builds the newest version with the changes that were just made. After building these changes, the suite runs tests on the application that was changed to catch bugs or mistakes that may occur. The suite launch emulators for all the main operating systems and goes through input process. After inputting the data the suite then validates results from LandPKS models and creates tables and stores results in the tables. This system allows changes and bugs to be tested constantly and the results stored for tracking purposes.

The LandPKS API on the backend is a single insertion and retrieval point. This allows anyone to send and receive data from a single point. This API integrates all the insertions and updates the plot areas whenever data is received. The API also integrates our models into the data as it received. The API allows all this multidirectional data flow to be unnoticeable and easily manipulated to the end user. The API also allows anyone to use our data in other applications with simple http requests. When a request for a site is received, the API retrieves all the relevant data and returns the results via the same query. Integrated into the LandPKS API is “API Explorer” which allows anyone to become acquainted with the “API” by showing the types of requests that can be passed as well as the responses received.

Brent Barnett is a dual undergrad student in the Chemical Engineering Department and the Computer Science Department at New Mexico State University. He is a transfer student from Austin, Texas. He has worked with automation and database engineering for six years. Prior to joining Jornada he worked for Versasuite and IBM as a software and database engineer respectively. His primary roles are build/test automation and LPKS API development. His interests include database development, prediction algorithms, process design and automation.

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Brent Barnett – API Data Manager

Land-Potential and Soil Texture

The knowledge engine of LandPKS supports land use planning, land restoration, future agricultural scenarios, climate change adaption, and conservation programs. However, a critical first step in using our integrated suite of smartphone applications includes the evaluation of soil and vegetation properties. As soil serves as the media for growth for all kinds of plants, identification of soil properties is vital for land managers, policy makers, and researchers in order for them to assess land potential.

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Soil texture is considered one of the soil’s most important properties, influencing nearly all soil processes and functions. Soil texture is defined by the relative fractions of sand, silt, and clay-sized particles in a soil sample. Soil is often divided into 12 soil texture classes, allowing for communication of soil type amongst land resource specialists. Each particle type has benefits to plant growth, yet sometimes can be unfavorable when only one soil class (all sand, for example) makes up the majority of the soil sample. For example, clay holds water well and is usually fertile. However, clays swell when they get wet (limiting the water available to plant roots), and harden when dry (becoming difficult to manage). Deep sands drain easily and do not hold water effectively. Silt-sized grains retain water and nutrients, but can easily become water-logged and prevent movement of water, air, and roots throughout the soil profile. While most soil types can be managed, often Loam is considered the most desirable for plant growth because Loam contains equal parts of sand, silt, and clay. Different particle sizes allow for air, water, and roots to easily move through the soil, with Loam having enough sand to drain well, yet also enough clay and silt to hold onto water and nutrients.

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The LandInfo module of LandPKS walks users through estimation of texture by probing and working the soil. Users are asked to take a sample and test it for grittiness (sand), smoothness (silt), and stickiness (clay). Currently, the LandInfo module streamlines steps from a soil texture-by-feel flow chart which tests the relative fraction of sand, silt, and clay. However, through feedback from our trainings we have learned that often the user questions accuracy of their own texture-by-feel estimates. Our next steps to help address this issue include: (1) evaluating the accuracy of these texture-by-feel estimates, and (2) improving our decision support tools to allow users additional manipulative tests shown to differentiate between texture classes. Look for the results of these steps in the near future!

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LandPKS for Soil Identification: Using Soil Texture and Color

Soil is one of the most important factors that control crop yields and land potentials. There are many possible soils with different properties within a given location. How to correctly identify soils and the subsequent soil properties is critically important for farmers, natural resource managers, policy makers, and scientists to make decisions and predictions regarding land suitability, productivity, profitability, and sustainability.

In our effort to help make soil identification in the field easier, LandPKS has two team members, Zhaosheng Fan and Samira Pakravan, who are working on improving our current LandInfo module, as well as developing a completely new module, SoilColor. Fan and Samira are currently focused on, 1) developing algorithms that can be used to identify correct soils with spatial location (latitude and longitude) and other easily-measured field observations input by LandPKS users (e.g., soil texture by depth), and 2) developing a SoilColor module that can be used to measure soil color in the field with a smartphone camera. Soil color is one of the most important attributes of the soil which provides useful information about many other significant soil properties, such as soil organic carbon content. The SoilColor module will use smartphone camera images of soil samples to measure the soil color. Once complete, LandPKS users will be able to identify soil color without the use of expensive soil color books.

Soil color app photos

Further, the two efforts mentioned above are tied together – the measured soil color with the SoilColor module can, in turn, be used by the soil-identification algorithms to further improve the accuracy of the identified soils. Once the correct soils are identified, the corresponding soil properties will be used to drive crop models to simulate crop yields, erosion, and crop-failure risks. Please do look for the release of SoilColor in the near future!

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Zhaosheng Fan – Postdoctoral Researcher
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Samira Pakravan – Soil Color Developer

The Role of LandPKS in Land Use Planning

In Tanzania, the Land Potential Knowledge System (LandPKS; landpotential.org) has been working with the National Land Use Planning Commission as well as USAID’s Land Tenure Assistance (LTA) Project to assist in effective land use planning and land tenure efforts.  LandPKS is a mobile app that helps users identify their soil, and the potential productivity and long term agricultural sustainability of that soil.  By identifying areas with sustainable agricultural potential, land use planners can integrate biophysical assessments into their participatory land use planning process.  The LTA project is helping to revise village-level land use plans. Once land use planning is complete, LTA is using a mobile technology called MAST (Mobile Application to Secure Tenure) to assist village members attain a CCRO (Certificate of Customary Right of Occupancy) for their farms and properties. By integrating LandPKS soil information into their land-use plans, LTA and other land use planners can make better decisions about which areas within a village are suitable for agriculture, and which are not. Focusing agricultural growth on soils suitable for sustainable agriculture not only increases farmer revenues and productivity, but saves other village land for other, less intensive uses such as grazing areas or forest reserves.

At the national-level, Dr. Stephen Nindi, Director General of the National Land Use Planning Commission, is working with the LandPKS team to implement LandPKS tools in the future for the national land use planning process. Tanzania currently uses a 6-step process for participatory land use management, involving community members and stakeholders at every step. On the biophysical side of land use planning process, Tanzania draws from the seven land capabilities classes. Categorizing the land into these seven classes helps planners to determine which livelihood activities are sustainable in which areas. For example, land capability class one refers to land that is suitable for all land uses with normal land management practices, such as flat, well drained and fertile land. LandPKS could play a key role in helping land use planners classify land into these seven classes based on soil texture, soil water holding capacity, potential erosion risk, and potential productivity. While soil texture is not the only important soil characteristic, it can be a critical predictor of a lands potential and its degradation risk. Implementing LandPKS in the land use planning process will simply, and cheaply, help the National Land Use Planning Commission include more biophysical data into their land use planning process.

Therefore, LandPKS has a role not only in improving sustainable land management for farmers and pastoralists, but also on a larger scale through the land use planning process. Matching appropriate land uses to their proper soil types can increase productivity and decrease land degradation, important goals for long-term environmental sustainability. For more information, please write to contact@landpotential.org

Nyamihuu, Tanzania – Comparing 3 LandPKS Plots

LandPKS Modeling for Agricultural Sustainability: Meet our Modelers

We are lucky at LandPKS to have a wonderful team of people who work on various aspects of LandPKS.  Behind the scenes, we have two dedicated modelers who are working to take LandPKS input and model future agricultural scenarios to help LandPKS users make more sustainable land management decisions.  While these types of outputs are not currently available on LandPKS, we hope that in the near future such modeled future scenarios will be delivered to LandPKS users on their phones.

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Dr. Won Seok Jang is a hydrologist and modeler for LandPKS based at the University of Colorado Boulder, in the Sustainability Innovation Lab at Colorado (SILC).  Won Seok’s primary roles include developing a modeling framework and assessing the effect of soil degradation on crop productivity and local/regional/global scale hydrological modeling for climate change impact assessment.  He currently uses EPIC (Environmental Policy Integrated Climate) model to estimate potential crop yield and soil erosion and is developing an EPIC parallel computing framework for global modeling with big data.  The goal of his research is to explore and measure the impact of climate change on food production worldwide, to develop multi-objective optimization of crop yield and minimization of soil erosion initially in Eastern Africa with a plan to expand to the United States and other global locations in the future.

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Dr. Tegenu Engda is a postdoctoral researcher, also located at the University of Colorado Boulder, in SILC. His primary role is to understand Soil-Plant-Water interactions under different environmental conditions. He mainly uses the EPIC model to investigate factors affecting crop yield trends including hydrologic processes, erosion and management practices in the case of East Africa. Currently, he is working on sensitivity analysis of EPIC input soil properties to better understand yield estimates across soil types.  Tegenu is also working on EPIC model calibration and nutrient dynamics exploration, as well as incorporation of local knowledge to improve EPIC yield calculations.

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